As mentioned in the previous sections, it is unclear why [1]} performed all experiments in a single session, rather than splitting it into shorter sub-sessions, as is unmistakenly indicated in [2]}. The only effect of increasing the sessions duration is to increase fatigue, which, in turn, decreases the subject's vigilance. This is a well-known issue in the cognitive neuroscience and human factors communities  , , , .
To provide some evidence of this to readers of the machine learning community, we attempt to quantify the impact of experiment duration on temporal correlation in EEG data and how this may inflate classification accuracy. Specifically, we measure the difference between the classification accuracy of 1D-CNN [1]} and chance accuracy on the BDB (with the same procedure explained in Section REF ) and RDVE (with block-level labels) datasets. Indeed, as mentioned earlier, these datasets contain neural responses which are not related to any visual class stimuli. Any classification rate above chance on these data can be accounted for by temporal correlation.
For this analysis EEG data is filtered between 14-70 Hz and results are computed on a per-subject basis, consistently with [1]}.
We compare our results with those obtained for the same 1D-CNN method in [1]}. Specifically, from [1]}, we report [1]}'s accuracy values are taken from Table 9 of [1]}'s manuscript and from Tables 41–45 of [1]}'s appendix. the performance (as average classification accuracy across all subjects) of the 1D-CNN method on the rapid-design (with block labels, as this is supposed to show temporal correlation) with both images and videos.
